Understanding Homoscedasticity in Multiple Regression

Consider a multiple regression analysis where you are investigating the relationship between a dependent variable, $Y$, and three independent variables, $X_1$, $X_2$, and $X_3$.

For the model to produce valid and reliable results, certain underlying assumptions must hold. One key assumption is that the residuals (the differences between observed and predicted values) exhibit constant variance, known as homoscedasticity. If this assumption is violated, it may lead to inefficient estimates and biased conclusions about the coefficients.

Which of the following statements best describes a violation of assuming homoscedasticity in a multiple regression context?